2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)最新文献

A Mathematical Model of the G1/S Transition for the Budding Yeast 芽殖酵母G1/S过渡的数学模型

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044536

P. Palumbo

引用次数: 0

SCADA Systems: Security Concerns and Countermeasures SCADA系统:安全问题和对策

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044495

Lourdes Cecilia Ruiz Salvador, Nguyen Huu Phuoc Dai, Rajnai Zoltán

引用次数: 0

Analyzing Immunomes using Sequence Embedding and Network Analysis 利用序列嵌入和网络分析分析免疫组

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044509

Kristina Motuzenko, Ilya Makarov

{"title":"Analyzing Immunomes using Sequence Embedding and Network Analysis","authors":"Kristina Motuzenko, Ilya Makarov","doi":"10.1109/SAMI58000.2023.10044509","DOIUrl":"https://doi.org/10.1109/SAMI58000.2023.10044509","url":null,"abstract":"The adaptive immune system helps us to resist the spread and eliminate potentially dangerous pathogens from an organism. This specific activity to pathogens is achieved through immune receptors such as B cell receptors (BCR) and their secreted version antibodies and T cell receptors (TCR). The sum of B cell and T cell receptors is the immune repertoire. The immune repertoire is the language of the immune system and its study would reveal information about antigen specificity, immune history, immune status, and therapeutics. CDR3 is the most variable part of TCRβ and makes the most significant contribution to the binding to the antigen. There are many Machine Learning and Deep Learning approaches for immune repertoire analysis. But the studies with attention-based model implementations lack. We take amino acid sequences of CDR3 fragments from TCRβ and use them as text for a state-of-the-art NLP model for the classification task. We choose ProtBert to obtain embeddings for a further downstream task to classify immune status. Using the BERT model did not yield significant results compared to the simpler model. Such a contradictory result can be explained by the fact that the attention mechanism could not catch high-level dependencies in sequences, and it is enough to use differences at the n-gram level to get a relatively good result.","PeriodicalId":179029,"journal":{"name":"2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116448061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-occurrence Networks for Word Sense Induction 词义归纳的共现网络

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044503

Innokentiy S. Humonen, Ilya Makarov

引用次数: 0

Dementia classification using attention mechanism on audio data 基于注意机制的音频数据痴呆分类

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044539

Shkhanukova Milana

引用次数: 0

Statistical Analysis of the Performance of the Dye-Sensitized Solar Cell – Thermoelectric Generator 染料敏化太阳能电池-热电发电机性能的统计分析

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044524

Z. Varga, Ervin Rácz

{"title":"Statistical Analysis of the Performance of the Dye-Sensitized Solar Cell – Thermoelectric Generator","authors":"Z. Varga, Ervin Rácz","doi":"10.1109/SAMI58000.2023.10044524","DOIUrl":"https://doi.org/10.1109/SAMI58000.2023.10044524","url":null,"abstract":"In this study the performance of a built Dye-Sensitized Solar Cell-Thermoelectric Generator system is investigated using conventional statistical analysis. The two main questions are (i) Whether there is a significant difference between the performance of the Dye-Sensitized Solar Cell and the performance of the Dye-Sensitized Solar Cell-Thermoelectric Generator hybrid system. (ii) Whether there is a significant relationship between the surface temperature and the performance of the hybrid system. If there is any significant relationship, how many temperature-difference it occurs. In order to achieve the above-mentioned aim, in case of the (i) independent samples T-test has been used and in case of (ii) Analysis of variance analysis have been used. According to the results, there is a significant difference between the performance of the Dye-Sensitized Solar Cell and the performance of the Dye-Sensitized Solar Cell-Thermoelectric Generator hybrid system. Because of the violation of the normality and the violation of the homogeneity, Welch-probe was applied and according to the results, there is a significant relationship between the performance of the hybrid system and the surface temperature of the solar cell. Bonferroni Post-Hoc test was applied, and the results showed that there is significant different between the maximum power output values of the hybrid system for each temperature.","PeriodicalId":179029,"journal":{"name":"2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114956893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Usage of Anytime Algorithm in Fault detection in Cloud Systems 任意时间算法在云系统故障检测中的应用综述

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044521

Farida Asadova, A. Várkonyi-Kóczy, Gábor Kertész, R. Lovas

引用次数: 0

Processing of collected data using an industrial 2D visual camera system 使用工业2D视觉相机系统处理收集的数据

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044529

V. Maxim, J. Ligus, J. Ligusova, Dávid Kovaľuk, Matúš Sabol

引用次数: 0

Engineering competencies expected in the digital working places 数字化工作场所所需的工程能力

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044531

Éva Beke

引用次数: 0

Combining Classical and Neural Approaches for Image Segmentation 结合经典和神经方法的图像分割

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2023-01-19 DOI: 10.1109/SAMI58000.2023.10044498

Márton Cserni, A. Rövid

{"title":"Combining Classical and Neural Approaches for Image Segmentation","authors":"Márton Cserni, A. Rövid","doi":"10.1109/SAMI58000.2023.10044498","DOIUrl":"https://doi.org/10.1109/SAMI58000.2023.10044498","url":null,"abstract":"In addition to detecting the 2D bounding boxes of objects in the image, instance segmentation determines on a pixel level, which pixel corresponds to the object. Although recent deep learning based instance segmentation methods are currently state of the art in accuracy, they are relatively slow, use massive computational resources, or are flat-out incapable of running in real-time on cheaper hardware. Classical algorithms can perform background segmentation on images made by infrastructure mounted cameras using far less computational power, however are less robust to changes in scene lighting. They are sensitive to shadows, and cannot perform pixel-level classification of detected objects. In this work, we compare the state of the art of deep learning based methods to classical solutions, and we present a hybrid CNN and classical algorithm based segmentation method, that is capable of running up to 71 frames per second on a standard PC. The method uses Yolov7 for object detection and performs segmentation based on background subtraction only within detected bounding boxes of objects. The background model is created by a classical background segmentation algorithm. The method works on an infrastructure mounted camera, and is robust versus changes in lighting, and even performs well on infrared images.","PeriodicalId":179029,"journal":{"name":"2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0